US 2004 presidential election data at state or county level

A sample of voting data from US states or counties (depending on data availability), sampled with probability proportional to number of votes. The sample was drawn using Tille's splitting method, implemented in the "sampling" package.

data(election)

Format

election is a data frame with 4600 observations on the following 8 variables.

County

A factor specifying the state or country

TotPrecincts

Number of precincts in the state or county

PrecinctsReporting

Number of precincts supplying data

Bush

Votes for George W. Bush

Kerry

Votes for John Kerry

Nader

Votes for Ralph Nader

votes

Total votes for those three candidates

p

Sampling probability, proportional to votes

election_pps is a sample of 40 counties or states taken with probability proportional to the number of votes. It includes the additional column wt with the sampling weights.

election_insample indicates which rows of election were sampled.

election_jointprob are the pairwise sampling probabilities and election_jointHR are approximate pairwise sampling probabilities using the Hartley-Rao approximation.

Source

.

Examples

data(election)
## high positive correlation between totals
plot(Bush~Kerry,data=election,log="xy")

## high negative correlation between proportions
plot(I(Bush/votes)~I(Kerry/votes), data=election)


## Variances without replacement
## Horvitz-Thompson type
dpps_br<- svydesign(id=~1,  fpc=~p, data=election_pps, pps="brewer")
dpps_ov<- svydesign(id=~1,  fpc=~p, data=election_pps, pps="overton")
dpps_hr<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40))
dpps_hr1<- svydesign(id=~1, fpc=~p, data=election_pps, pps=HR())
dpps_ht<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=ppsmat(election_jointprob))
## Yates-Grundy type
dpps_yg<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=ppsmat(election_jointprob),variance="YG")
dpps_hryg<- svydesign(id=~1,  fpc=~p, data=election_pps, pps=HR(sum(election$p^2)/40),variance="YG")

## The with-replacement approximation
dppswr <-svydesign(id=~1, probs=~p, data=election_pps)

svytotal(~Bush+Kerry+Nader, dpps_ht)
#>          total      SE
#> Bush  64518472 2604404
#> Kerry 51202102 2523712
#> Nader   478530  102326
svytotal(~Bush+Kerry+Nader, dpps_yg)
#>          total      SE
#> Bush  64518472 2406526
#> Kerry 51202102 2408091
#> Nader   478530  101664
svytotal(~Bush+Kerry+Nader, dpps_hr)
#>          total      SE
#> Bush  64518472 2624662
#> Kerry 51202102 2525222
#> Nader   478530  102793
svytotal(~Bush+Kerry+Nader, dpps_hryg)
#>          total      SE
#> Bush  64518472 2436738
#> Kerry 51202102 2439845
#> Nader   478530  102016
svytotal(~Bush+Kerry+Nader, dpps_hr1)
#>          total      SE
#> Bush  64518472 2472753
#> Kerry 51202102 2426842
#> Nader   478530  102595
svytotal(~Bush+Kerry+Nader, dpps_br)
#>          total      SE
#> Bush  64518472 2447629
#> Kerry 51202102 2450787
#> Nader   478530  102420
svytotal(~Bush+Kerry+Nader, dpps_ov)
#>          total      SE
#> Bush  64518472 2939608
#> Kerry 51202102 1964632
#> Nader   478530  104373
svytotal(~Bush+Kerry+Nader, dppswr)
#>          total      SE
#> Bush  64518472 2671455
#> Kerry 51202102 2679433
#> Nader   478530  105303